{"id":3971,"date":"2025-09-29T17:20:11","date_gmt":"2025-09-29T09:20:11","guid":{"rendered":"https:\/\/saludpcb.com\/zh\/?p=3971"},"modified":"2025-09-29T17:39:51","modified_gmt":"2025-09-29T09:39:51","slug":"ai-compute-power-pytorch","status":"publish","type":"post","link":"https:\/\/saludpcb.com\/zh\/ai-compute-power-pytorch\/","title":{"rendered":"\u7b97\u529b AI Compute Power \u5168\u958b\uff012025 PyTorch \u52a0\u901f\u6280\u5de7\u8207 AI \u7b97\u529b\u5be6\u6230"},"content":{"rendered":"\n<hr class=\"wp-block-separator alignwide has-text-color has-palette-color-1-color has-alpha-channel-opacity has-palette-color-1-background-color has-background is-style-wide\"\/>\n\n\n\n<p>\u5728\u4eba\u5de5\u667a\u6167\u7684\u6642\u4ee3\uff0c<strong>AI \u7b97\u529b\uff08AI Compute Power\uff09<\/strong> \u5df2\u7d93\u6210\u70ba\u63a8\u52d5\u6280\u8853\u7a81\u7834\u7684\u6838\u5fc3\u71c3\u6599\u3002\u5f9e\u8a13\u7df4\u5927\u578b\u8a9e\u8a00\u6a21\u578b\u5230\u904b\u884c\u5373\u6642\u7684\u96fb\u8166\u8996\u89ba\u61c9\u7528\uff0c\u6c92\u6709\u5f37\u5927\u7684\u7b97\u529b\u652f\u6490\uff0c\u5275\u65b0\u5c31\u7121\u6cd5\u843d\u5730\u30022025 \u5e74\uff0c\u96a8\u8457 PyTorch 2.x \u7684\u6210\u719f\u8207\u786c\u9ad4\u52a0\u901f\u7684\u666e\u53ca\uff0c\u5982\u4f55\u6700\u5927\u5316 AI \u7b97\u529b\u3001\u91cb\u653e\u6846\u67b6\u6f5b\u80fd\uff0c\u5df2\u7d93\u6210\u70ba\u7814\u7a76\u8005\u8207\u958b\u767c\u8005\u6700\u95dc\u5fc3\u7684\u8ab2\u984c\u3002\u672c\u6587\u5c07\u5e36\u4f60\u6df1\u5165 PyTorch \u7684\u52a0\u901f\u6280\u5de7\u8207\u6548\u80fd\u5be6\u6230\uff0c\u5f9e\u74b0\u5883\u8a2d\u5b9a\u5230\u7a0b\u5f0f\u512a\u5316\uff0c\u5168\u9762\u638c\u63e1\u8b93 AI \u7b97\u529b\u5168\u958b\u7684\u65b9\u6cd5\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"891\" src=\"https:\/\/saludpcb.com\/zh\/wp-content\/uploads\/2025\/09\/Unleashing-AI-Compute-Power-Accelerating-PyTorch-for-Brilliant-Performance-in-2025.jpg\" alt=\"AI Compute Power\" class=\"wp-image-3978\" title=\"\" srcset=\"https:\/\/saludpcb.com\/zh\/wp-content\/uploads\/2025\/09\/Unleashing-AI-Compute-Power-Accelerating-PyTorch-for-Brilliant-Performance-in-2025.jpg 1024w, https:\/\/saludpcb.com\/zh\/wp-content\/uploads\/2025\/09\/Unleashing-AI-Compute-Power-Accelerating-PyTorch-for-Brilliant-Performance-in-2025-768x668.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div class=\"wp-block-rank-math-toc-block\" id=\"rank-math-toc\"><h2>\u5167\u5bb9<\/h2><nav><ul><li class=\"\"><a href=\"#fir\">\u4ec0\u9ebc\u662f AI Compute Power\uff1f<\/a><\/li><li class=\"\"><a href=\"#ai-compute-power-\u8207-py-torch-\u7684\u95dc\u4fc2\">AI \u7b97\u529b\u8207 PyTorch \u7684\u95dc\u4fc2<\/a><\/li><li class=\"\"><a href=\"#\u74b0\">\u958b\u767c\u74b0\u5883<\/a><\/li><li class=\"\"><a href=\"#\u5c08\u6848\u7d50\u69cb\">\u5c08\u6848\u7d50\u69cb<\/a><\/li><li class=\"\"><a href=\"#c\">Code<\/a><\/li><li class=\"\"><a href=\"#\u6548\u80fd\u5be6\u6230\u4e09\u6b65\u9a5f\">\u63d0\u5347\u6548\u80fd\u4e09\u6b65\u9a5f<\/a><\/li><li class=\"\"><a href=\"#\u7d50\u8ad6\">\u7d50\u8ad6<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"fir\">\u4ec0\u9ebc\u662f AI Compute Power\uff1f<\/h2>\n\n\n\n<p>\u5728\u4eba\u5de5\u667a\u6167\u7684\u767c\u5c55\u4e2d\uff0c\u6211\u5011\u7d93\u5e38\u807d\u5230 <strong>\u300c\u7b97\u529b\u300d<\/strong> \u9019\u500b\u8a5e\uff0c\u800c <strong>AI Compute Power\uff08AI \u7b97\u529b\uff09<\/strong>\uff0c\u5c31\u662f\u9a45\u52d5\u4e00\u5207\u6df1\u5ea6\u5b78\u7fd2\u8207\u6a21\u578b\u8a13\u7df4\u7684\u6838\u5fc3\u300c\u5f15\u64ce\u99ac\u529b\u300d\u3002\u5b83\u4ee3\u8868\u4e86\u4e00\u500b\u7cfb\u7d71\u5728\u57f7\u884c AI \u4efb\u52d9\uff08\u5982\u6a21\u578b\u8a13\u7df4\u3001\u63a8\u7406\u3001\u6578\u64da\u8655\u7406\uff09\u6642\u7684 <strong>\u8a08\u7b97\u80fd\u529b<\/strong>\u3002<\/p>\n\n\n\n<p><strong>AI Compute Power \u5305\u542b\u54ea\u4e9b\u9762\u5411\uff1f<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u786c\u9ad4\u5c64\u9762<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>CPU<\/strong>\uff1a\u8ca0\u8cac\u901a\u7528\u904b\u7b97\uff0c\u4f46\u8655\u7406\u901f\u5ea6\u6709\u9650\u3002<\/li>\n\n\n\n<li><strong>GPU<\/strong>\uff1a\u6df1\u5ea6\u5b78\u7fd2\u7684\u4e3b\u529b\uff0c\u64c5\u9577\u5927\u898f\u6a21\u4e26\u884c\u904b\u7b97\u3002<\/li>\n\n\n\n<li><strong>TPU \/ NPU \/ MPS<\/strong>\uff1a\u5c08\u70ba AI \u6253\u9020\u7684\u52a0\u901f\u6676\u7247\u3002<\/li>\n\n\n\n<li><strong>\u8a18\u61b6\u9ad4\u8207\u983b\u5bec<\/strong>\uff1a\u5f71\u97ff\u8cc7\u6599\u50b3\u8f38\u8207\u5b58\u53d6\u901f\u5ea6\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u8edf\u9ad4\u5c64\u9762<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>\u6846\u67b6\u512a\u5316<\/strong>\uff1a\u4f8b\u5982 PyTorch 2.x \u7684 <code>torch.compile<\/code> \u6216\u81ea\u52d5\u6df7\u5408\u7cbe\u5ea6 AMP\u3002<\/li>\n\n\n\n<li><strong>\u5e73\u884c\u904b\u7b97<\/strong>\uff1a\u591a GPU \u6216\u5206\u6563\u5f0f\u8a13\u7df4\u3002<\/li>\n\n\n\n<li><strong>\u7b97\u5b50\u6700\u4f73\u5316<\/strong>\uff1a\u50cf cuDNN\u3001MKL\u3001FlashAttention \u7b49\u5eab\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u6548\u80fd\u8861\u91cf<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>FLOPS<\/strong>\uff08\u6bcf\u79d2\u6d6e\u9ede\u904b\u7b97\u6b21\u6578\uff09<\/li>\n\n\n\n<li><strong>\u8a13\u7df4\u6642\u9593 \/ \u63a8\u7406\u5ef6\u9072<\/strong><\/li>\n\n\n\n<li><strong>\u80fd\u6548\u6bd4<\/strong>\uff08Performance per Watt\uff09<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>\u7c21\u55ae\u4f86\u8aaa\uff0cAI Compute Power \u5c31\u662f <strong>AI \u7684\u99ac\u529b<\/strong>\u3002\u7b97\u529b\u8d8a\u5f37\uff0c\u5c31\u80fd\u66f4\u5feb\u8a13\u7df4\u6a21\u578b\u3001\u8655\u7406\u66f4\u5927\u6578\u64da\uff0c\u4e26\u4e14\u5728\u5be6\u969b\u61c9\u7528\u4e2d\u5c55\u73fe\u66f4\u597d\u7684\u6548\u80fd\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ai-compute-power-\u8207-py-torch-\u7684\u95dc\u4fc2\"><strong>AI \u7b97\u529b<\/strong>\u8207 PyTorch \u7684\u95dc\u4fc2<\/h2>\n\n\n\n<p>\u5728\u4eba\u5de5\u667a\u6167\u7684\u4e16\u754c\u88e1\uff0c<strong>AI Compute Power\uff08AI \u7b97\u529b\uff09<\/strong>\u5c31\u50cf\u662f\u300c\u5f15\u64ce\u99ac\u529b\u300d\uff0c\u800c <strong>PyTorch<\/strong>\u5247\u662f\u300c\u99d5\u99db\u9019\u53f0\u5f15\u64ce\u7684\u5de5\u5177\u300d\u3002\u5169\u8005\u7dca\u5bc6\u7d50\u5408\uff0c\u624d\u80fd\u8b93\u6a21\u578b\u8a13\u7df4\u548c\u63a8\u7406\u767c\u63ee\u6700\u4f73\u6548\u80fd\u3002<\/p>\n\n\n\n<p><strong>PyTorch \u5982\u4f55\u767c\u63ee AI Compute Power\uff1f<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u786c\u9ad4\u52a0\u901f\u652f\u63f4<\/strong><br>PyTorch \u53ef\u4ee5\u5728 <strong>CPU\u3001GPU\uff08CUDA\uff09\u3001Apple Silicon\uff08MPS\uff09<\/strong> \u7b49\u4e0d\u540c\u8a2d\u5099\u4e0a\u904b\u884c\uff0c\u8b93\u76f8\u540c\u7a0b\u5f0f\u78bc\u9748\u6d3b\u5229\u7528\u53ef\u7528\u7b97\u529b\u3002<\/li>\n\n\n\n<li><strong>\u7b97\u529b\u512a\u5316\u5de5\u5177<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong><code>torch.compile<\/code>\uff08PyTorch 2.x\uff09<\/strong>\uff1a\u81ea\u52d5\u7de8\u8b6f\u8207\u512a\u5316\u6a21\u578b\uff0c\u63d0\u9ad8\u8a13\u7df4\u548c\u63a8\u7406\u901f\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u81ea\u52d5\u6df7\u5408\u7cbe\u5ea6\uff08AMP\uff09<\/strong>\uff1a\u5728\u4fdd\u6301\u6e96\u78ba\u7387\u7684\u524d\u63d0\u4e0b\uff0c\u4f7f\u7528\u66f4\u4f4e\u7cbe\u5ea6\u904b\u7b97\uff0c\u63d0\u5347\u7b97\u529b\u4f7f\u7528\u6548\u7387\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5206\u6563\u5f0f\u8207\u5927\u898f\u6a21\u8a13\u7df4<\/strong>\n<ul class=\"wp-block-list\">\n<li>\u900f\u904e <code>torch.distributed<\/code>\uff0cPyTorch \u53ef\u4ee5\u652f\u63f4\u591a GPU\u3001\u591a\u7bc0\u9ede\u8a13\u7df4\uff0c\u5145\u5206\u64f4\u5c55\u7b97\u529b\u4ee5\u8655\u7406\u5927\u578b\u6a21\u578b\uff0c\u4f8b\u5982\u5927\u578b\u8a9e\u8a00\u6a21\u578b\uff08LLMs\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>\u7c21\u55ae\u4f86\u8aaa\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u6c92\u6709 AI Compute Power\uff0cPyTorch \u5c31\u7121\u6cd5\u9ad8\u901f\u904b\u884c\u3002<\/strong><\/li>\n\n\n\n<li><strong>\u6c92\u6709 PyTorch\uff0c\u7b97\u529b\u7684\u6f5b\u80fd\u5c31\u7121\u6cd5\u5145\u5206\u767c\u63ee\u3002<\/strong><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u74b0\">\u958b\u767c\u74b0\u5883<\/h2>\n\n\n\n<p>\u5de5\u6b32\u5584\u5176\u4e8b\uff0c\u5fc5\u5148\u5229\u5176\u5668\u3002\u8b93\u6211\u5011\u5feb\u901f\u8a2d\u7f6e\u4f60\u7684\u958b\u767c\u74b0\u5883\u3002<\/p>\n\n\n\n<p><strong>\u4f5c\u696d\u7cfb\u7d71<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>macOS \/ Linux \/ Windows<\/strong> \u7686\u53ef<\/li>\n<\/ul>\n\n\n\n<p><strong>\u5b89\u88dd Python<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u5efa\u8b70\u7248\u672c\uff1a<strong>Python 3.9 ~ 3.12<\/strong><\/li>\n\n\n\n<li>\u6aa2\u67e5\u662f\u5426\u5df2\u5b89\u88dd\uff1a<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\"><code>python3 --version<\/code><\/code><\/pre>\n\n\n\n<p>\u5982\u679c\u6c92\u6709\u5b89\u88dd\uff0c\u8acb\u5230 <a class=\"\" href=\"https:\/\/www.python.org\/downloads\/\" target=\"_blank\" rel=\"noopener\">Python \u5b98\u65b9\u7db2\u7ad9<\/a> \u4e0b\u8f09\u4e26\u5b89\u88dd\u3002<\/p>\n\n\n\n<p><strong>\u5b89\u88dd VSCode<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4e0b\u8f09\uff1a<a href=\"https:\/\/code.visualstudio.com\/\" target=\"_blank\" rel=\"noopener\">Visual Studio Code<\/a><\/li>\n\n\n\n<li>\u5b89\u88dd <strong><a href=\"https:\/\/saludpcb.com\/zh\/coding-pycharm-with-python\/\">Python<\/a> \u64f4\u5145\u5957\u4ef6<\/strong>\uff08Microsoft \u5b98\u65b9\u63d0\u4f9b\uff09<\/li>\n<\/ul>\n\n\n\n<p><strong>Git\uff08\u7248\u672c\u63a7\u5236\u7528\uff09<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u6aa2\u67e5\u662f\u5426\u5b89\u88dd\uff1a<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\"><code>git --version<\/code><\/code><\/pre>\n\n\n\n<p>\u82e5\u672a\u5b89\u88dd\uff0c\u53ef\u5230 <a class=\"\" href=\"https:\/\/git-scm.com\/\" target=\"_blank\" rel=\"noopener\">Git \u5b98\u65b9\u7db2\u7ad9<\/a> \u4e0b\u8f09\u3002<\/p>\n\n\n\n<p><strong>\u5b89\u88dd PyTorch<\/strong><\/p>\n\n\n\n<p>\u524d\u5f80\u5b98\u65b9<a href=\"https:\/\/pytorch.org\/\" target=\"_blank\" rel=\"noopener\">\u7db2\u7ad9<\/a>\u6839\u64da\u4f60\u7684\u5e73\u53f0\uff08\u4f5c\u696d\u7cfb\u7d71\uff09\u3001\u5305\u7ba1\u7406\u5de5\u5177\uff08pip\/conda\uff09\u548c CUDA \u7248\u672c\uff08\u5982\u679c\u6709 NVIDIA GPU\uff09\u9078\u64c7\u5c0d\u61c9\u7684\u547d\u4ee4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\"># Install (CPU version, works for everyone)\npip3 install torch torchvision torchaudio\n\n# Install with CUDA 12.1 (for NVIDIA GPU users)\npip3 install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu121\n\n# Install via Conda (with CUDA 12.1 support)\nconda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia<\/code><\/pre>\n\n\n\n<p><strong>\u9a57\u8b49\u5b89\u88dd\u53ca\u8a2d\u5099<\/strong><\/p>\n\n\n\n<p>\u6253\u958b\u4f60\u7684 Python \u74b0\u5883\uff08Jupyter Notebook, VS Code, PyCharm \u7b49\uff09\uff0c\u57f7\u884c\u4ee5\u4e0b\u4ee3\u78bc\u6aa2\u67e5\u8a2d\u5099\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">import torch\n\nprint(f\"PyTorch version: {torch.__version__}\")\nprint(f\"CUDA (NVIDIA GPU) available: {torch.cuda.is_available()}\")\nif torch.cuda.is_available():\n    print(f\"CUDA device name: {torch.cuda.get_device_name(0)}\")\n\nprint(f\"MPS (Apple Silicon) available: {torch.backends.mps.is_available()}\")\n\n# Select which device to use\ndevice = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\nprint(f\"Using device: {device}\")<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u5c08\u6848\u7d50\u69cb\"><strong>\u5c08\u6848\u7d50\u69cb<\/strong><\/h2>\n\n\n\n<p>\u5728\u958b\u59cb\u5be6\u4f5c\u4e4b\u524d\uff0c\u5148\u5efa\u7acb\u4e00\u500b\u6e05\u6670\u7684<a href=\"https:\/\/saludpcb.com\/zh\/python-projects-with-venv\/\">\u5c08\u6848\u7d50\u69cb<\/a>\uff0c\u80fd\u5e6b\u52a9\u4f60\u66f4\u597d\u5730\u7ba1\u7406\u7a0b\u5f0f\u78bc\u3001\u74b0\u5883\u8207\u4f9d\u8cf4\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"bash\" class=\"language-bash\">my_ai_project\/         # \u5c08\u6848\u6839\u76ee\u9304\n\u251c\u2500\u2500 my_ai_env\/         # \u865b\u64ec\u74b0\u5883\u8cc7\u6599\u593e\uff08\u4e0d\u6703\u4e0a\u50b3\u5230 Git\uff09\n\u251c\u2500\u2500 app.py             # \u4e3b\u7a0b\u5f0f\n\u251c\u2500\u2500 requirements.txt   # \u5957\u4ef6\u4f9d\u8cf4\u6e05\u55ae\n\u2514\u2500\u2500 .gitignore         # Git \u5ffd\u7565\u898f\u5247<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"c\">Code<\/h2>\n\n\n\n<p><strong>PyTorch 2.x \u65b0\u6b66\u5668\uff1a<code>torch.compile<\/code><\/strong><br>\u81ea\u5f9e PyTorch 2.0\uff0c\u5b98\u65b9\u63a8\u51fa\u4e86 <code><strong>torch.compile<\/strong><\/code>\uff0c\u53ef\u4ee5\u81ea\u52d5\u512a\u5316\u6a21\u578b\u7684\u57f7\u884c\u6548\u80fd\uff0c\u5f80\u5f80\u80fd\u5e36\u4f86 <strong>30% ~ 200% \u7684\u52a0\u901f<\/strong>\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">import torch\nimport torch.nn as nn\n\n\nmodel = nn.Sequential(\nnn.Linear(784, 512),\nnn.ReLU(),\nnn.Linear(512, 512),\nnn.ReLU(),\nnn.Linear(512, 10)\n)\n\n\n# \u4f7f\u7528 compile \u512a\u5316\ncompiled_model = torch.compile(model)<\/code><\/pre>\n\n\n\n<p><strong>\u6df7\u5408\u7cbe\u5ea6\u8a13\u7df4 (AMP)<\/strong><br><strong>\u81ea\u52d5\u6df7\u5408\u7cbe\u5ea6 (Automatic Mixed Precision, AMP)<\/strong> \u662f\u53e6\u4e00\u500b\u52a0\u901f\u5229\u5668\u3002\u5b83\u5141\u8a31\u6a21\u578b\u5728\u4fdd\u6301\u6e96\u78ba\u5ea6\u7684\u524d\u63d0\u4e0b\uff0c\u4f7f\u7528\u66f4\u4f4e\u7cbe\u5ea6\u7684\u6d6e\u9ede\u6578\uff08\u4f8b\u5982 <code>float16<\/code>\uff09\u9032\u884c\u904b\u7b97\uff0c\u5927\u5e45\u63d0\u5347\u6548\u80fd\uff0c\u5c24\u5176\u5728 GPU \u4e0a\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code lang=\"python\" class=\"language-python\">scaler = torch.cuda.amp.GradScaler()\n\n\nfor X, y in train_loader:\noptimizer.zero_grad()\nX, y = X.to(device), y.to(device)\nwith torch.cuda.amp.autocast():\npred = model(X)\nloss = loss_fn(pred, y)\nscaler.scale(loss).backward()\nscaler.step(optimizer)\nscaler.update()<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u6548\u80fd\u5be6\u6230\u4e09\u6b65\u9a5f\">\u63d0\u5347\u6548\u80fd\u4e09\u6b65\u9a5f<\/h2>\n\n\n\n<ul start=\"1\" class=\"wp-block-list\">\n<li><strong>\u9078\u64c7\u6700\u4f73\u786c\u9ad4\u8a2d\u5099<\/strong>\uff1a<code>cpu \/ cuda \/ mps<\/code> \u9748\u6d3b\u5207\u63db\u3002<\/li>\n\n\n\n<li><strong>\u5584\u7528 PyTorch 2.0<\/strong>\uff1a\u4e00\u884c <code>torch.compile<\/code> \u8b93\u6a21\u578b\u52a0\u901f\u3002<\/li>\n\n\n\n<li><strong>\u6df7\u5408\u7cbe\u5ea6\u8a13\u7df4 (AMP)<\/strong>\uff1a\u5c11\u91cf\u4ee3\u78bc\u6539\u52d5\uff0c\u8a13\u7df4\u901f\u5ea6\u98db\u8e8d\u63d0\u5347\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u5728 AI \u7af6\u8cfd\u6216\u5c08\u6848\u958b\u767c\u4e2d\uff0c\u9019\u4e9b\u6280\u5de7\u80fd\u8b93\u4f60\u66f4\u5feb\u5b8c\u6210\u5be6\u9a57\uff0c\u6436\u5f97\u5148\u6a5f\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u7d50\u8ad6\">\u7d50\u8ad6<\/h2>\n\n\n\n<p>AI \u7684\u6642\u4ee3\u5df2\u7d93\u4f86\u81e8\uff0c\u4f46\u53ea\u6709\u61c2\u5f97 <strong>\u6548\u80fd\u8abf\u6821\u8207\u7b97\u529b\u512a\u5316<\/strong> \u7684\u5de5\u7a0b\u5e2b\uff0c\u624d\u80fd\u771f\u6b63\u91cb\u653e\u786c\u9ad4\u7684\u6f5b\u529b\u3002PyTorch \u5728 2025 \u5e74\u5df2\u4e0d\u53ea\u662f\u300c\u7814\u7a76\u5de5\u5177\u300d\uff0c\u66f4\u662f <strong>\u7814\u7a76\u5230\u7522\u54c1\u843d\u5730\u7684\u6a4b\u6a11<\/strong>\u3002<\/p>\n\n\n\n<p>\u5982\u679c\u7b2c\u4e00\u7bc7\u6587\u7ae0\u8b93\u4f60\u80fd\u300c\u5165\u9580\u4e26\u8dd1\u51fa\u6a21\u578b\u300d\uff0c\u90a3\u9ebc\u9019\u4e00\u7bc7\uff0c\u5c31\u662f\u5e6b\u4f60\u628a\u5f15\u64ce\u5347\u7d1a\uff0c\u8b93\u4f60\u7684 AI <strong>\u8dd1\u5f97\u66f4\u5feb\u3001\u66f4\u7a69\u3001\u66f4\u8070\u660e<\/strong>\u3002<\/p>\n\n\n\n<p>\u4e0b\u4e00\u7bc7\uff0c\u6211\u5011\u5c07\u5e36\u4f60\u63a2\u7d22 <strong>\u76f8\u4f3c\u5ea6\u8207 AI \u8a8d\u77e5\uff1aPyTorch \u5982\u4f55\u7406\u89e3\u300e\u76f8\u4f3c\u300f\uff1f<\/strong>\uff0c\u4e00\u8d77\u9081\u5411\u66f4\u667a\u6167\u7684 AI \u958b\u767c\u4e4b\u65c5 \u3002<\/p>\n\n\n\n<hr class=\"wp-block-separator alignwide has-text-color has-palette-color-1-color has-alpha-channel-opacity has-palette-color-1-background-color has-background is-style-wide\"\/>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u4eba\u5de5\u667a\u6167\u7684\u6642\u4ee3\uff0cAI \u7b97\u529b\uff08AI Compute Power\uff09 \u5df2\u7d93\u6210\u70ba\u63a8\u52d5\u6280\u8853\u7a81\u7834\u7684\u6838\u5fc3\u71c3\u6599\u3002\u5f9e\u8a13\u7df4\u5927\u578b\u8a9e [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3978,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,87],"tags":[27,21,44,9,86,11],"class_list":["post-3971","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-python-ai","tag-c","tag-ide","tag-programming-language","tag-python","tag-rtos","tag-tutorial"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/posts\/3971","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/comments?post=3971"}],"version-history":[{"count":9,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/posts\/3971\/revisions"}],"predecessor-version":[{"id":3986,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/posts\/3971\/revisions\/3986"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/media\/3978"}],"wp:attachment":[{"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/media?parent=3971"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/categories?post=3971"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saludpcb.com\/zh\/wp-json\/wp\/v2\/tags?post=3971"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}